Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure

Aslan, Simge Nur; Uçar, Ayşegül; Güzeliş, Cüneyt

doi:10.1109/inista49547.2020.9194644

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Humanoid robots NAO [16][17][18][19][20][21]; PEPPER [22][23][24][25][26][27][28]; ROBOTIS OP3 [29]; INMOOV [30]; KIRO [31]; ARASH [32]; RAPIRO [33]; BRIAN 2.1 [34]; KISMET [35].…”

Section: Type Of Robot Modelsunclassified

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

Ragno,

Borboni,

Vannetti

et al. 2023

Sensors

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Cyber-physical or virtual systems or devices that are capable of autonomously interacting with human or non-human agents in real environments are referred to as social robots. The primary areas of application for biomedical technology are nursing homes, hospitals, and private homes for the purpose of providing assistance to the elderly, people with disabilities, children, and medical personnel. This review examines the current state-of-the-art of social robots used in healthcare applications, with a particular emphasis on the technical characteristics and requirements of these different types of systems. Humanoids robots, companion robots, and telepresence robots are the three primary categories of devices that are identified and discussed in this article. The research looks at commercial applications, as well as scientific literature (according to the Scopus Elsevier database), patent analysis (using the Espacenet search engine), and more (searched with Google search engine). A variety of devices are enumerated and categorized, and then our discussion and organization of their respective specifications takes place.

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“…Humanoid robots NAO [16][17][18][19][20][21]; PEPPER [22][23][24][25][26][27][28]; ROBOTIS OP3 [29]; INMOOV [30]; KIRO [31]; ARASH [32]; RAPIRO [33]; BRIAN 2.1 [34]; KISMET [35].…”

Section: Type Of Robot Modelsunclassified

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

Ragno,

Borboni,

Vannetti

et al. 2023

Sensors

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“…Computer vision technologies are generally used in applications as done in this study. For example, computer vision has been used in robotic applications for tasks such as obstacle avoidance and navigation (Chang, 2010;Pandey & Gelin, 2017), human-robot interaction (Le et al, 2018;Yavşan & Uçar, 2016), object detection for assisting robots, recognition (Martinez-Martin & Del Pobil, 2017), and object recognition for capture (Aslan et al, 2020;2021;Ku et al, 2017;Levine et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network models for efficient object recognition with humanoid robots

Aslan

Uçar

Güzeliş

2021

Journal of Information and Telecommunication

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Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure, based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models, a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models, such as , in terms of training and validation accuracy and loss, parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently, the proposed models can be considered promising powerful models for object recognition with humanoid robots.

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Malaysian Coins Recognition Using Machine Learning Methods

Zainuddin

Azhari

Hashim

et al. 2021

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)

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Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure

Cited by 8 publications

References 16 publications

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

New convolutional neural network models for efficient object recognition with humanoid robots

Malaysian Coins Recognition Using Machine Learning Methods

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